Assessing counterparty risk at private companies in energy industry A descriptive survey of credit models potx

Industrial and Financial Economics
Master Thesis No 2003:44






Assessing counterparty risk at private companies in energy industry

A descriptive survey of credit models





Kristina Papanyan

Graduate Business School
School of Economics and Commercial Law
Göteborg University
ISSN 1403-851X
Printed by Elanders Novum


iii

Acknowledgments

Hereby I would like to express my gratitude to Professor Göran Bergendahl, for
the vocational guidance and promotional recommendations.

I am grateful also to Professor Ted Lindblom for the assistance in arranging the
interview with Mr. Mikael Jednell, a power trader, whose useful opinion I
largely relied on.
Many thanks to all the lecturers and administrative staff for the friendly and
academic atmosphere at the School.

Special thanks to Mrs. Ann McKinnon for her devoted and prompt assistance
with any question applied.

iv
Abstract

Within the scope of this master thesis the author aims to perform an overview
of contemporary credit risk measurement and management models on the
subject of their application in energy trading sector. For that task, selected
models are considered and the advantages and drawbacks for the particular
application are discussed. The study is supported with specialists’ opinion and
an example from successful energy trading practice from US energy industry.

The study also intends to prepare a theoretical framework for undertaking a
further large-scale study among Swedish power traders. Regarding the last
ambition, author’s outlook is guided by energy market surveys and reports of
relevant authorities and energy companies in Sweden. It is also supported with
insights about the market obtained through an interview with a power trader at
one of the leading energy trading companies in Sweden. Materials obtained for
the present study are confined to those available in the English language.


v
Table of contents

1. Introduction 1

1.1 Background 3
1.2 Problem discussion 4
1.3 Purpose 7
1.4 Scope and limitation 7
1.5 Reliability and validity 9
1.6 Thesis outline 9
2. Methodology
11
2.1 Research approach 11
2.2 Data 12
2.3 Research design 12
2.3.1 Descriptive survey 13
2.3.2 Case study 13
3. Theoretical framework
15
3.1 Traditional approaches to credit valuation 15
3.1.1 Expert systems 15
3.1.2 Credit-scoring systems 16
3.1.3 Rating systems 16
3.2 Selected credit risk models for private companies 17
3.2.1 Altman’s Z-score for private companies 19
3.2.2 KMV’s EDF for private companies 20
3.2.3 Moody’s RiskCalc
TM
for Private Companies: Nordic Region 21
3.2.4 Summary credit risk elements and risk-measurement systems 24
3.3 Current trends in addressing credit risk 25
4. Contemporary credit risk mitigation approaches within energy sector
26
4.1 General considerations about credit risks in energy sector 26

4.2 Ameren Energy: an example of successful business practice 27
4.3 Portfolio approach for CRM at energy companies 30
5. Credit risk approached by Swedish energy sector: case study
32

vi
5.1 Market and Players: issues & developments 32
5.2 Assessing credit risk by energy traders 34
5.3 Interview 35
6. Summary and conclusions
37
6.1 Which model to choose? 38
6.2 Enterprise-Wide Risk Management - new business culture 39
6.3 Contribution 39
6.4 Line for further research 40
Reference list
41
Articles, research papers and reports 42
Internet sources 43
Selected definitions 44
APPENDIX I 45
APPENDIX II 46
APPENDIX III 49


1
1. Introduction
Industrial companies have recently faced additional issues of dealing with
foreign markets and regulations together with recent technological advances,
tendencies to economic globalization and overall cross-border expansion for

new business benefits. Companies have to closely scrutinize their more
concentrated and often distant credit risks representing one of their main
hindrances to growth Due to the improving economies’ openness and
competition,. Key reasons for recent intensively addressed credit risk
management issues, which many academics agree upon, could be summarized
as follows:

1. Challenging economic conditions and structural increase in bankruptcies,
reflected in ”stronger mandates for transparency into risk and balance sheet
health”
1
,
2. Disintermediation and deregulation encouraging innovations and enabling
new entrants to act in various economic sectors, by changing the outlook for
role of trading and other mark-to-market activities in the firm
2
,
3. More competitive margins and relative maturity of many of the industries,
4. Declining and volatile values of collateral as well as the substantial increase
of collateral agreements,
5. The growth of off-balance-sheet derivatives and respective risk-return
analysis,
6. Advances in analytical techniques and methodologies: econometric
techniques, neural networks, optimization models, portfolio management
approach etc,
7. New regulatory developments and business evidences in financial risk
management, i.e. BIS capital adequacy recommendations, robust control
across firms, standardization of financial instruments and risk reporting.

Credit risk is a complex category and sometimes represents a greater challenge

than both market risk (to predict when and under which conditions a
counterparty might default), and the purely endogenous operational risk. Credit
risk undeniably depends on market risk, but while market risk can be made
homogeneous by category, like for example, interest rate risk, foreign exchange
risk, credit risk is so to speak much more personalized. At the same time in
energy industry, for example, electricity producers and traders show high
performance sensitivity to market conditions, i.e. electricity price fluctuations,
which makes credit risk and market risk inseparable for strategic analysis and
resumes their joint modeling.

1

2
ibid

2
Another aspect of assessing credit risk is evaluating each counterparty
individually or at a combined risk-portfolio level. The former approach is
known as traditional, based on credit expert opinion, and is presently
considered as a passive credit risk management tool while encounting for a
numerous valuation methods and techniques. Managing credit risk within a
portfolio is a relatively recent approach. The groundwork in this area belongs to
H. Markowits, “Portfolio Selection”, Journal of Finance, 1952.
Further to the increased application of portfolio methods in credit instruments’
valuations, recent practice within corporate risk management reveals a growing
interest for integrated risk management at entire company level rather than
determining and managing different risks at divisional level. This approach is
known as Enterprise-Wide Risk Management (EWRM), where much of the
efforts of companies’ management is put into the integration of existing risk
modeling tools, and aggregate stress testing of various risks. “EWRM system

may be necessary to pull together all the different threads”
1
.

There are different ways of managing credit risks for different companies: for
financial institutions the mechanisms of handling credit risk issues are mainly
embedded in various credit derivatives, while for non-financial companies
those are mostly involved in the legibly formulated contract terms. At the same
time, however, we are observing erasing the conceptual distinctions between
financial and nonfinancial companies due to the same more competitive
environment and globalization processes.

It is a known fact that generally speaking industrial companies are not well-
equipped in the credit risk measurement area can also be because their
potential losses are easier mitigated due to the fact that their credit risks are
relatively low. “Trade receivables are generally high-quality assets because
companies are very reluctant to jeopardize their relationships with the
partners”
2
. In addition, trade receivables of industrial companies are relatively
short-term in nature and thus the collection procedure is relatively easier.

However, credit risk of trade intermediaries, i.e. power traders, not being
backed with as large tangible assets as energy generators, and earning a
competitive profit margin on energy trade, might be considered as a category of
players needing to model their credit risks at a most advanced level by
replicating the already mature financial companies’ expertise.

The present study addresses the above underlined issues in a more detail while
having a particular focus on credit risk issues in the energy sector.

1

2
Caouette et all, 1998 p. 48

3
1.1 Background

After several tarnishing bankruptcies in the US energy industry, i.e. Enron and
Pacific Gas & Electric company (PG&E), and the subsequent series of credit
rating downgrades by Rating Agencies, many industrial companies started to
realize that one of their most important risks, counterparty risk, is significantly
undermanaged. While market risk is the most watchful and largest risk faced by
energy companies, particularly for gas and power marketers, credit risk is the
next important factor.
When considering credit risk issues on the Swedish energy market, it can be
said that most of them are related to the recent electricity market deregulation
in 1996, continuing regulation and system development, redistribution of
productive forces among market participants etc. Along with its positive
contributions for healthy market competition, deregulation also created a lot of
tasks necessary in developing an efficient market mechanism, and hence a
highly liquid electricity trade. The opportunity of using financial derivatives to
hedge the ‘dry-years’ enables the protection of the energy companies’ profit.
However, this market, i.e. trading at Nord Pool – Nordic Energy Exchange, and
OTC market, needs further improvement with respect to trading terms and
achieving better liquidity of traded contracts. For instance, among the Nordic
countries presently forming a common electricity trade area, the Swedish
electricity market is far more centralized with respect to energy productive

forces. It is evident that electricity producing/generating companies generally
face less risks than trading companies because the formers are integrated with
their own supply/trading companies, and that they trade or hedge at NordPool
more or less the excess or the shortage of the necessary power. Besides, while
big energy producers face counterparty risk with a limited number of partners -
mostly from NordPool - the largest volume of energy trade is subject to risks
on the OTC market. It should however be mentioned that the present level of
bilateral trading is decreasing in favor of NordPool due to the tendency of
designing customer-tailored contracts which are gradually becoming a part of
trading instruments at NordPool because of their increasing recognition by
market participants.

Presently a number of analytical methodologies corporate risk management
software solutions are widely available for application at various economic
areas. Among these are integrated risk modeling packages for financial
institutions, investment and insurance companies, multinational corporations as
well as industry-tailored risk valuation methodologies. These risk management
solutions and frameworks are based on notable advances in option pricing
theory, appearance of new tools like VaR and its variations, and newly
designed financial instruments, as for example energy derivative contracts.

4
Despite the fact that best known credit risk models were initially developed for
financial institutions, with their large customer credit information, large
industrial corporations also increasingly benefit from these model applications.
The specific feature to differentiate between financial institutions’ and
industry-wide approaches to credit risk assessment is that the formers dispose
large databases of customer credit information, and are the first directly facing
the effect of unfavorable economic changes in form of customers’ defaults of
both high frequency and severity. Distinction between financial and non-

financial companies is necessary to point out because the formers have
different financial statement characteristics: on average they have more a
leveraged structure and because of their risk-taking function are thoroughly
regulated with respect to capital requirements. Non-financial (industrial)
companies are traditionally backed with relatively stable value bearing assets
against short liquidity problems and receivables collection issues, and thus their
operations are, not generally, perceived to be as risky as those at financial
institutions.

The above mentioned issues relating to the importance of credit risk
measurement and mitigation among power traders, have contributed to the
formulation of the problem for the analysis and study purpose to be explored
within the present thesis.


1.2 Problem discussion

Many energy market specialists presently point to the importance of design and
implementation of appropriate credit risk management systems within energy
industry. It is reflected in a conceptual shift from focusing on receivables
collection as one of few reported financial statement lines pointing to the size
of carried counterparty risk. Nowadays industrial companies recognize that the
“replacement costs” of long-term contracts carry significantly larger loss
potential.

Measuring counterparty credit risk involves capturing the threat of potential
future exposure, specifically, how much the counterparties could owe to a
given company in the event of solitary or mass default. A significant part of
this risk is likely to be the replacement cost of the long-term contracts, very
common to energy trade. Analysts following energy industry point that while

risk managers at energy firms are aware of the necessity to improve their firm’s
credit risk management capabilities by closer monitoring, managing, and
mitigating them, most managers still remain focused on current exposure
measurement, i.e., current mark-to-market exposure, plus outstanding
receivables, and collateral management. The problematic side of this approach

5
is the extensive attention to the presently more quantifiable risks which falls
short in providing an acceptable indication of credit risk at future points.
Hence, many economy theorists and practitioners currently exploit measures
capturing potential credit exposure, given potential and seasonal fluctuations of
electricity price
1
as well as trying to capture the maximum likely potential
exposure or probable maximum level of losses.

Taking into account business characteristics of the energy industry, many
analysts define mitigation and limiting exposure to energy market volatility and
optimization of electricity generation and distribution asset profitability
2
as the
most vital factors for company prosperity. However, these issues seem to be
crucial for the industry in general or electricity generators and network owners,
while power traders can focus on achieving somewhat different goals.

Thr Swedish energy sector, as any other country, has its own specific market
structure, regulation and traditional operating relationships among the players.
Presently, in this sector there is no revealed signs of potential exposure in form
of massive unpayment by energy end-users, both households and enterprises.
Besides which the country has sources for large energy imports which makes

the energy supply nearly insensitive to seasonal fluctuations in energy
generation. These factors significantly decrease the pressure on energy price
and make risk of rationing nearly non-existent. Among the current issues can
be mentioned the increased flexibility of production sight versus consumptions
areas where despite of occasional interruptions the failure risk is low; partial
dissatisfaction of end-users with the high energy prices caused by still potential
rationing risk.

Along with the mentioned factors one should notice that few highly vertically
integrated market leaders, namely the four major producers amounting about
90% of total electricity generation in the country, are in a much more favorable
situation rather than a large number of electricity traders (about 130
companies) managing relatively small-size portfolios of end-user energy
provision contracts. Those energy traders who have their own energy
generating capacities can better match their output potential to the forecasted
consumption levels, and thus better fulfill their “balance resposibility”.

It has already been estimated that electricity price will continue to rise in
Sweden “as a result of European integration, stricter environmental demands
and, in the longer term, by the need for new capacities
3
. Among various issues
under current consideration, the energy market leader Vattenfall specifically

1

2

3
Vattenfall, Electricity Market Report 2002 p.3

6
points out that “Nordic power markets perform well at wholesale level, but
from a customer’s perspective there is room for improvement”
1
. This refers to a
quite complicated schedule of bill payments due estimated on historical average
rather than actual consumption.

If considering a case of an extremely “bad-year”, where the existing capacities
would be insufficient and accompanied with some other coincided difficulties a
possibility of prices crisis could occur (the case is rather hypothetical), for
which power traders could be the first to face difficulties. Here one can point
out that against extreme credit events, i.e. defaults of high frequency and
severity due to critically dry-year, or using insurance terminology - catastrophe
risk, companies have various opportunities to insure themselves. In general,
electricity trading with the underlying product’s high seasonal price volatility,
large number of buyers and sellers, and fungible physical products, stimulates
development of futures trading. In these conditions the concept of Power
Exchange with efficient and liquid clearing and netting system allows for
significant counterparty risk mitigation.

Regarding the current state in the Swedish electricity market, and in the Nordic
market as a whole, it is a fact that despite the already existing specialized
marketplace for organized electricity trade, i.e. NordPool, according to Svenska
Kraftnät, only 35% of actual trade is performed through NordPool. The
prevailing trade is arranged via OTC bilateral/trilateral contracts, where
counterparty risks are still of much importance.

It should be mentioned that there is a great variety of literature and explorative

studies of credit risk management issues for public financial institutions and its
valuation. As it has already been mentioned, credit risk issues are traditionally
less vital for industrial company’s risk profile than for financial institutions.
However, industrial companies are currently feeling uncomfortable with their
credit risk mitigation approaches and recognize a lack of self-contained default
model frameworks and methodological approaches, despite the several recent
initiatives from the leading rating and consulting agencies for assigning credit
scores reflecting default probabilities for private companies.

The above stated concerns about counterparty default risk faced in general by
energy industry have induced the author of this paper to focus on the existing
credit risk models and their industry applications. The particular interest
towards the energy industry became the reason for considering Swedish power
traders as the risk takers. When summarizing the above section, several
questions appear to be crucial for this study:


1
ibid, p.11.

7
1. What is the importance for power traders to model internally their
counterparty risk?
2. To what extent can power traders assess their credit risks to their non-listed
counterparties by applying credit risk models (appropriate analytical
techniques or simulation software)?
3. If not measured internally, how do energy traders assess their counterparty
risk?

Thus, the main problem of this study is to contribute to the addressed issue of

internal credit risk valuation procedures by energy traders with respect to their
end-user counterparties.


1.3 Purpose

To solve the problems formulated in the previous section, the author aims to
review traditional and contemporary credit risk assessing methodologies on the
subject of their industry application, that is presently less empowered with
analytical procedures and sophisticated credit risk mitigating tools. For the
reasons discussed in the previous section, the author chose the energy sector as
an application industry, i.e. counterparty risks faced by power traders with
respect to their customers, energy end-users.

The author perceives that the mentioned purpose would be achieved through an
evolving solution to the following objectives:

1. Revealing the importance of assessing counterparty risk faced by energy
traders in Sweden,
2. Reviewing selected credit risk models with respect to their possible
application in energy industry as well as presenting successful
implementation of internally developed methodologies and expert opinions,
3. Addressing possible ways of transferring counterparty risk as an alternative
form of managing them.


1.4 Scope and limitation

The present paper covers methodologies for assessing default probabilities of
private non-rated companies (energy end-users) faced by their service providers

(power traders).
Public companies or those listed at stock exchanges are believed to be well
evaluated by the market, and thus their probability of default is presumably

8
easier to ascertain by analyzing their stock performance and volatility, as well
as by studying yield curves and spreads on their corporate bonds.
Conversely, private companies usually have a limited number of owners with
untransferable shareholdings; their financial stability greatly depends on the
wealth position of the company’s owners, and more importantly, their earnings
and financial stability is quite volatile. Despite many of them presently being
monitored by industry analysts, credit bureaus, consultative agencies, and
various supervisory authorities, the common opinion is that the market reacts to
their performance issues with a significant delay.

Because of better opportunities for monitoring by financial analysts, public
companies are not covered by this study. Instead, private enterprises and
individual consumers are forming a customer focus group to be tested on
potential risk of default. This fact is supposed to be an additional complication
into the current analysis. This paper also does not intend to cover the start-up
companies, recently merged or restructured ones, because there are difficulties
with valuing highly volatile financial statements and tracing their performance
trend and average growing rates.

It should be mentioned that energy traders as well as their counterparties could
be of different ownership structure, represent retail or whole trade sector, be
integrated as both energy generation and supplier/distributor or be only a retail
supplier with or without balance responsibility, be managers of portfolios of
end-user contracts, etc. In this situation it is quite difficult to identify a
particular group of companies on which to focus the study. Instead, the author

decided to define the area of operations which are certainly covered by
respective companies depending on their market share and thus the level of
their internal credit risk assessment. In particular, the area of operations
covered within this study is the energy wholesale trading, which in turn
supposes modeling of company risks at different managerial levels, namely at
the front-office, middle-office and back-office levels.
Front-office mainly performs strategic role of transation and deal pricing,
modeling optimal bids, developing portfolio optimization techniques and
reporting on the overall market positions of a company;
Middle-office has the key function of measuring and controlling credit risk,
extensively employs risk metrics for statistical and correlation analysis of
energy trade developments, as well as modeling asset’s productivity and reports
on performance of the limits set.
Back-office is rather the execution chain of company’s policy. It is the
performer of the transaction along with their physical and financial settlement,
deals with collection issues, as well as reports on transaction tenor thus
promoting to company’s responsiveness to the market evolvements.


9
As long as the studied issues of counterparty risk measurement and mitigation
are assessed within the operations performed by the middle-office at energy
trading companies, the present survey addresses both their problems and tools
available for respective solutions. At the same time a concrete company
apparently adapts relevant risk models to their risk patterns to achieve better
application outcomes.

Another aspect to mention is the application of credit derivatives (CDs) which
proved to be an effective hedging tool against unexpected market outcomes and
credit risk mitigation. They are designed to minimize an exposure from loans,

investments, guarantees and other customer financing commitments. While
realizing the significance of applying CDs to mitigate electricity price risk,
within the facet of this study the specific features and applicable strategies of
using CDs are not studied. Instead, they are perceived as an effective tool useful
at the later stage of credit risk analysis after the expected and potential credit
exposure is estimated and needs to be mitigated.


1.5 Reliability and validity

The author’s view is that the reliability of this study is supported by the prudent
expertise and high reputation of presented credit risk models’ developers,
models’ high popularity among financial institutions and overall strong
performance. For the validity of this study contributes the fact that the models
are presented accurately, and are supported by the critical opinion and
comments from leading market specialists and academics within the field.
Besides, regarding the analysis of energy sector, the studied publications are
complemented by an interview conducted with a expert following energy
trading at one of the leading energy trading companies in Sweden.
The author believes that the feasibility for the present study would be achieved
by obtaining answers for the stated objectives and by the ability to underline
useful and contributive features of existing approaches for industry application.


1.6 Thesis outline

The purpose of current study and the problems’ solution are supposed to be met
via following steps of analysis:
1. Studying academic literature and modern theoretical approaches,
successful business models and internal practices for measurement and

mitigating counterparty risk, starting with expert system to more advanced
statistical models.

10
2. Reviewing modern risk valuation approaches and internally developed risk
measures among some of the energy industry players.
3. Revealing the important issues related to counterparty risk in energy trading
sector of Sweden, and referring to the issues of the Nordic electricity market
where relevant to the subject studied.
4. Presenting energy market specialists’ opinion about the practicability of
applying one or another model in companies’ internal risk valuation
methodology.
5. Make an concluding analysis about the practicability of applying certain
credit risk measuring tools for counterparty risk mitigation by energy
traders.

To perform the underlined tasks, selected credit risk models are suggested by
the author to assess counterparty default issues for energy industry, with
respect to counterparty risks taken by power traders against electricity end-
users, where the credit events themselves presently can be considered as rather
hypothetical.
The sections “Writing research theses or dissertations” on the webpage of the
University of New Castle upon Tyne, and “Advice on Academic Writing” on
the webpage of University of Toronto were used as a guideline for structuring
the present paper was used. Of great assistance was the book by Swales and
Feak
1
with a lot of useful information about the contents of academic paper
specific sections, important features and criteria to be met. The book has many
useful and explanatory tips for writing and structuring an academic paper. At

the same time the general sequence of headlines within the paper is organized
the way the author believes is relevant for the stated problem discussion and
elaboration.








1
See the Reference list for books

11
2. Methodology
Hereinafter follows a description of how the study was conducted. It includes
approaches and methods employed, sources of obtained data and the general
argumentation for performing the study.


2.1 Research approach

When surveying selected models for credit risk modeling the author
intentionally does not present a comprehensive discussion of how the models
had been developed, and the analysis of how they are working, since these
models are quite accessible and widely discussed. Most of the issues having
direct or indirect effect on credit risk measurement have been underlined in the
problem background and problem discussion sections and are further referred
to within the context of model exploitation. At the same time the aim of this

study is free of a criticism of the previously conducted studies, it includes only
the commonly perceived advantages and drawbacks of the models.

Thus instead of discussing the models themselves, an attempt is made to carry
out a survey about the contemporary tools for obtaining solutions to the stated
problems, and classify the models from the point of their relevance to energy
sector application. It seems to be more important to address particular models’
underlying concepts and assumptions, positive features and shortcomings in an
extracted form, as well as to discuss and analyze the ways of these models’
possible adaptation and applicability by industrial players, namely within the
energy sector.
The author hopes that this way the conducted study can contribute to a
refreshment of appropriate theoretical and practical background for energy
traders’ internal credit risk modeling through adaptation of available tools to
the needs and objectives of their companies. Besides, despite dealing with
quantitative models the present study is believed to meet the requirements of a
qualitative study in form of a structured analytical discussion of the stated
problems, and in this way contributing to the purpose achievement.

Issues presented in the problem discussion and the formulated purpose indicate
a deductive approach employment. According to P. Hall, 1994 “deductive
approach seeks particular applications of general principles which science has
uncovered”
1
. We are given theoretical models for credit risk measurement,
widely applicable at financial institutions, which has to be deductively applied
for various industry players for the better management of credit risk issues. To

1
Peter Hall, “Innovation, Economics and Evolution” New York: Harvester Wheatsheaf, 1994.

12
approach formulated problems based on the concerns announced by different
energy sector representatives, the author considers it important to present a
review of existing theoretical framework and practical approaches for the
problem solution.

Numerous sources of information, expert opinions and initiative studies are
available about credit risk management, among them are also those that are
sometimes not enough verifiable and authenticated in electronically available
articles, company publications in internet. The authors view considers that it is
the ‘right’ outlook to present and make parallels between different views. By
doing so one can effectively address the vital problems formulated by
companies. The academic literature, of course, serves as an supporting tool for
understanding the underlying relationship of the stated problems.


2.2 Data

With respect to sources of information, the most primary data concerning credit
risk assessment by industrial companies was obtained through academic
literature study, initiative research papers and explorative articles, conference
materials, certain companies’ internally developed methodologies available on
their web pages, as well as sources of financial information at Gothenburg
University’s library.
In particular, the sources of information processed to assess the defined
problems are studies and methodologies developed by leading financial
academics and analysts, one of the most reputable rating and consulting
company (Moody’s Investors Service), one well known American electricity
producing and trading corporation (Ameren Energy Corp.), and a joint study of

financial analysts following credit risk issues in energy sector.
With respect to information sources about the Swedish energy sector, there
were various but limited materials in English language from Svenska Kraftnät,
energy companies webpages, initiative market research studies by Vattenfall
AB and other energy companies, electricity market reports, etc.


2.3 Research design

As already mneioned, any study that aims to make a contribution to the
analyzed problem begins with an extensive literature study within the subject as
well as of some related areas and applications. This way the study can avoid
repetition and the rediscovering of “known” relationships. Also in the present
work, an extensive literature study in credit risk management (CRM) was
conducted with respect to reviewing the traditional methods for ongoing and

13
long-term credit decisions up till the examination of some of the contemporary
internally developed companies’ CRM frameworks.


2.3.1 Descriptive survey

This study is assumed to be of a nature of a descriptive survey with respect to
presenting an overview of the most advanced and self-contained credit risk
measuring and mitigating methodologies. Thr author also reviewed the most
popular software solutions for credit risk modeling in energy industry, and
presents and short description of the selected ones
1
.

The author also believes that a descriptive survey is the right form of research
design for the present paper. By the definition of descriptive study it is “a study
that tries to reveal patterns associated with a specific issues without an
emphasis on pre-specified hypotheses”. Sometimes these types of studies are
called hypothesis generating studies (to contrast them with hypothesis testing
studies)”
2
. A descriptive study can aim to:
1. help in planning resource allocation
2. identify areas for further research
3. provide informal diagnostic information.
In a broad sense, a descriptive survey focuses on revealing the issues, preparing
a background for their possible solutions rather than testing the relationships or
quantifying the problem. Within such a study one can estimate the development
of the problem, the possible tools for its solution, as well as presenting critical
attitudes and expert opinion about the issue.

This study can be considered as a preparation for a large-scale research about
energy trading companies’ credit risk issues arising due to temporary liquidity
problems caused by seasonally fluctuating electricity price.


2.3.2 Case study

Besides the descriptive nature of this study it aims to create a credit risk
modeling framework for the application for Swedish energy traders. This case
study is supported by an interview with a power trader at one of the Swedish
electricity trading companies. As previously mentioned, interviews represent an
important step of any survey, carrying opportunity to assess the stated

1
See Appendix II
2


14
questions by transposing the summarized practical knowledge and expertise
from the relevant survey group of credit professionals, as in the present case.
This interview, being performed according to the questionnaire
1
, had essential
contribution to a deeper understanding of the current issues of the Swedish
electricity market as well as clarification of certain practical details not
highlighted in general market publications or possibly inaccessible to the
author, because of language considerations. It should be noticed as well that the
mentioned interview presents the only source of primary data obtained by the
author.

1
See Appendix I

15
3. Theoretical framework
In this chapter the author reviews the theoretical background of the issues of
modelling credit risks associated with private companies.


3.1 Traditional approaches to credit valuation

Most credit scoring models are either expert systems, based on judgmental
criterion and attempt to duplicate a credit analyst’s decision making process, or
statistical systems, relying on quantitative factors that according to the model
vendor’s research, are indicators of default
1
. An example of expert systems
include Moody’s RiskScore® etc, while examples of credit risk quantifying
models include Zeta®, KMV’s Credit Monitor®, Moody’s RiskCalc®, and
Standard & Poor’s CreditModel®.

Traditional, or presently referred to as passive, approach to credit risk
management encompasses expert systems, credit-scoring and rating systems. A
very detailed methodology of assessing a single counterparty’s credit risk is
presented by H. A. Schaeffer, 2000
2

. Another methodology of assigning credit
scores was developed by Altman (1968) presently extended into a wide
framework of credit valuation, the advantageous simplicity of which competes
with statistically complicated approaches. The most advanced model of this
type is the Moody’s RiskCalc
TM
. Rating systems are based on transaction or
counterparty credit limit, defined by customer’s externally assigned credit
rating, transaction’s tenor, and cumulative exposure level.
A very convenient, extensive and self-contained review of the credit risk
models is presented by Saunders, 1998 and an extended discussion about the
models and issues around them is done by Caouette et all, 1998
3
.


3.1.1 Expert systems

This valuation system is quite expensive to maintain, due to high costs of
preparing and maintaining a qualified and experienced personnel, training
expenses etc. Within this system credit decisions depend on lending officers’
appraisal of counterparty’s creditworthiness by evaluating certain parameters
(business reputation, borrower’s capital structure, capacity or ability to repay,
collateral, and cycle of economic conditions).

1
“Rating Credit Risk”, p.7
2
see Reference list.
3

see Reference list.

16
Professional investment management firms, for example, operate with far less
manpower than do banks and, in general, have less confidence in their ability to
select the right borrowers. Therefore the formers have begun to incorporate
credit skills that are normally associated with banks. The biggest concern with
this approach is that portfolio concentration cannot be avoided, because to
become a good expert an individual must focus on a relatively narrow set of
companies within a single industry. At the same time according to portfolio
theory, breadth of activity, i.e., diversification is more important than the
selection of individual risks. This approach is out of rules to a typical financial
analyst as with a potential loan extension to unknown sectors, regions or
customer groups. However, at present financial institutions are increasingly
inclined to syndicate, securitize, or otherwise diversify their originated
portfolios.


3.1.2 Credit-scoring systems

One type of these systems is the well-known Altman’s Z-score model
1
. As with
most credit risk assessing models, the objective of scoring systems is to
maximize measurable risk. Credit policy devises the ways to increase the
predictive capability of credit analysis by estimating counterparties’ probability
of default, and in this way to enhance the amount of risk controlled. Although
the timing of evolution to a score based approach and technology has been
vastly different for the credit card, mortgage, auto and commercial lending
industries, the results have been similar: it led to a faster, more consistent,

unbiased, and more accurate approach to lending. The simple way of presenting
the underlying process of accessing the probability of default is:

P(A and B)= Corr (A,B) X [P(A)(1-P(A))]
1/2
X [P(B)(1-P(B))]
1/2
+ P(A)X P(B)

If the two default events are independent, then the correlation is 0; in this case
“purchasing a credit protection” or in other words, risk diversification will
bring the probability of loss from P (A) down to P(A)xP(B).


3.1.3 Rating systems

This approach entails a risk-weighted asset valuation to calculate capital
reserves against unexpected losses, and loan loss reserves against expected loan
losses. Ratings are simple way to transform a discrete event (default) to a

1
Altman, 1968.

17
continuous variable (rating change)
1
. It is well-known that a continuous
variable is easier to handle and to obtain the dynamics than discrete events.
Some apprehension with this concept exists due to a quite widely shared
opinion that there is no useful information to be obtained from ratings, as they

are too slow to adjust and reflect rating agencies’ management as much as true
credit changes. Others show that there is little information in rating upgrade (all
the information has already been incorporated into market prices) but there is
some in rating downgrade
2
. Other authors have addressed stability (or
instability) of rating migrations and established a methodology to adapt rating
migrations to changes in a business cycle, the country or industrial sector
3
.
Nevertheless, in general this approach of treating business counterparties by the
credit rating assigned by reputable rating agencies are of high practicability for
the companies and many of them heavily rely on external ratings in their credit
decisions.
Finally it should be mentioned about the common disadvantage of the
traditional credit rating systems: they typically do not provide with a strong
form of differentiation across the borrowers and the relevant risky assets, and
do not offer a consistent framework for forecasting and avoiding credit losses.


3.2 Selected credit risk models for private companies

Credit risk assessing tools and methodologies available to financial institutions
are extensively addressed in the academic literature. This is mainly because the
largest databases of credit performance and default frequencies are maintained
by financial institutions, and thus they have larger opportunities for theoretical
and technical explorations of credit risk issues.
Presently several conceptual tools are available to measure counterparty’s
creditworthiness, on individual basis or within a portfolio, depending on the
purpose of measurement. Some models stress the importance of constructing a

distribution of portfolio possible outcomes, while others focus on assigning
credit ratings to companies according to the quality of their outstanding debt
etc. All these models can classified into two main groups:
Structural models, focusing on evaluation of company’s strength by looking at
the financial statements, and
Default intensity models, considering the default as a random variable with the
roots covered by economic factors rather than internal company’s structure,
being similar to actuarial approach in insurance.


1
Caouette et all, 1998 p. 203.
2
ibid.
3
Saunders, 1998 p. 49 referring to Nickell et all, 1998.

18
In connection with the mentioned trends, the largest industrial companies
presently have their own subsidiaries to handle the broad financial aspects of
their activity, e.g., corporate treasury departments, insurance companies,
investment companies and even their own banks enabling them to access and
authority to operate in financial and capital markets. From this credit
information, financial information and qualitative appraisal of the majority of
companies is generated by various multinational agencies and/or locally at
each country’s official business statistic report in form of master file data,
combination of application and demographic data, as well as one relatively new
source as transaction data, which is predictive for certain applications. Master-
file data enable the users to score their customers on a monthly basis, according
their “payment behavior”, while transaction data enable credit grantors to score

customers dynamically. With the latter approach lenders can react quickly to
changes in customer profile and change customer treatment as required
1
.

However, it should also be mentioned that the procedures of dealing with credit
risk issues is still a matter of internal practice, expertise, financial power and
sometimes conservative confidentiality at most companies in any business
field.

Until recently industrial companies had not considering credit issues as an
integral part of their business portfolios and instead tended to be conservative
in their credit policy by making credit judgments on an individual basis. In case
of increased risks over certain limits, industrial companies were handling them
by demanding more strict credit terms: collateral, deposits, or up-front
payments
2
.
Historically, producers were managing their credit risks by demanding letters
of credit (L/Cs) or were selling the trading receivables to factor companies.
However, the bigger a company becomes the more is its own potential for
managing receivables collection and developing more extended terms for
contracting, payments etc.
Presently credit risks are rarely considered on a stand-alone basis because of
greater interrelations between the same industry participants within the
production chain, and thus greater correlation or common responsiveness to
same macroeconomic conditions. Thus, application of portfolio methods for
mitigating credit risks is becoming more and more popular among industrial
companies. These more active credit risk measurement and mitigating
techniques assume regular credit reviews, collateral agreements, downgrade

triggers, termination clauses, and usage of credit derivatives
3
.

1
Hollis, p.172.
2
Caouette et all, 1998, p 48.
3


19
The models presented in the following sections are selected on the basis of their
increasing popularity and feasibility for an appropriate application by industry
players.


3.2.1 Altman’s Z-score for private companies

Altman’s Z-score model
1
is based on accounting data and applies a multivariate
approach built on the values of both ratio-level and dichotomous univariate
measures. These values are combined and weighted to produce a credit risk
score that best discriminates between firms that fail and those that do not. This
kind of analysis is possible because failing firms show ratios and financial
trends that are different from financially sound companies. Credit experts
would reject a credit application of their prospective partners or subject them to
increased scrutiny if the actual credit score of a credit applicant falls below a
critical benchmark. The Z-score model was constructed using multiple

discriminant analysis that analyzes a set of variables to maximize the between-
group variance while minimizing the within-group variance
2
.
To arrive at final profile of variables the following procedures are used:
1. Testing statistical significance of various alternative functions, including
determination of the relative contributions of each independent variable,
2. Estimation of inter-correlations among the model variables,
3. Observation of the predictive accuracy of the model,
4. Judgment of the credit analyst.

The basic Z-score model has endured until the present day and has also been
applied to private companies, manufacturing firms, and emerging market
companies. The model uses five ratios contributing to estimating the
company’s credit score:
1. Working Capital/Total Assets, which is a measure of company’s net liquid
assets relative to total capitalization.
2. Retained Earnings/Total Assets. This is a measure of cumulative profits
which appears to be greater for mature companies, and thus at some extent
discriminates against young players more subjected to failure
3
.
3. EBIT
4
/Total Assets, as a measure of productivity power of the company’s
assets.
4. Book Value of Equity
5
/Book Value of Liabilities, reflecting the leverage
level of the company.

1
Altman, 1968, 1993.
2
Caouette et all, 1998, p. 115.
3
“The Failure Record”, Dun & Bradstreet New York, 1997.
4
Earnings Before Interest and Taxes
5
For public companies the measure of Market Value of Equity is used.